no code implementations • 16 Apr 2025 • Lei Sun, Andrea Alfarano, Peiqi Duan, Shaolin Su, Kaiwei Wang, Boxin Shi, Radu Timofte, Danda Pani Paudel, Luc van Gool, Qinglin Liu, Wei Yu, Xiaoqian Lv, Lu Yang, Shuigen Wang, Shengping Zhang, Xiangyang Ji, Long Bao, Yuqiang Yang, Jinao Song, Ziyi Wang, Shuang Wen, Heng Sun, Kean Liu, Mingchen Zhong, Senyan Xu, Zhijing Sun, Jiaying Zhu, Chengjie Ge, Xingbo Wang, Yidi Liu, Xin Lu, Xueyang Fu, Zheng-Jun Zha, Dawei Fan, Dafeng Zhang, Yong Yang, Siru Zhang, Qinghua Yang, Hao Kang, Huiyuan Fu, Heng Zhang, Hongyuan Yu, Zhijuan Huang, Shuoyan Wei, Feng Li, Runmin Cong, Weiqi Luo, Mingyun Lin, Chenxu Jiang, Hongyi Liu, Lei Yu, WeiLun Li, Jiajun Zhai, Tingting Lin, Shuang Ma, Sai Zhou, Zhanwen Liu, Yang Wang, Eiffel Chong, Nuwan Bandara, Thivya Kandappu, Archan Misra, Yihang Chen, Zhan Li, Weijun Yuan, Wenzhuo Wang, Boyang Yao, Zhanglu Chen, Yijing Sun, Tianjiao Wan, Zijian Gao, Qisheng Xu, Kele Xu, Yukun Zhang, Yu He, Xiaoyan Xie, Tao Fu, Yashu Gautamkumar Patel, Vihar Ramesh Jain, Divesh Basina, Rishik Ashili, Manish Kumar Manjhi, Sourav Kumar, Prinon Benny, Himanshu Ghunawat, B Sri Sairam Gautam, Anett Varghese, Abhishek Yadav
This paper presents an overview of NTIRE 2025 the First Challenge on Event-Based Image Deblurring, detailing the proposed methodologies and corresponding results.
no code implementations • 13 Apr 2025 • Lei Sun, Yuhan Bao, Jiajun Zhai, Jingyun Liang, Yulun Zhang, Kaiwei Wang, Danda Pani Paudel, Luc van Gool
Low-light image enhancement (LLIE) aims to improve the visibility of images captured in poorly lit environments.
1 code implementation • 3 Apr 2025 • Nedko Savov, Naser Kazemi, Mohammad Mahdi, Danda Pani Paudel, Xi Wang, Luc van Gool
To this end, we annotate the behavior and controls of 974 virtual environments - a dataset that we name RetroAct.
1 code implementation • 24 Mar 2025 • Chenfei Liao, Kaiyu Lei, Xu Zheng, Junha Moon, Zhixiong Wang, YiXuan Wang, Danda Pani Paudel, Luc van Gool, Xuming Hu
We then introduce a robustness benchmark that evaluates MMSS models under three scenarios: Entire-Missing Modality (EMM), Random-Missing Modality (RMM), and Noisy Modality (NM).
1 code implementation • 23 Mar 2025 • Yue Li, Qi Ma, Runyi Yang, Huapeng Li, Mengjiao Ma, Bin Ren, Nikola Popovic, Nicu Sebe, Ender Konukoglu, Theo Gevers, Luc van Gool, Martin R. Oswald, Danda Pani Paudel
In order to power the proposed methods, we introduce SceneSplat-7K, the first large-scale 3DGS dataset for indoor scenes, comprising of 6868 scenes derived from 7 established datasets like ScanNet, Matterport3D, etc.
no code implementations • 14 Feb 2025 • Asen Nachkov, Danda Pani Paudel, Jan-Nico Zaech, Davide Scaramuzza, Luc van Gool
Differentiable simulators have recently shown great promise for training autonomous vehicle controllers.
no code implementations • 15 Jan 2025 • Qi Ma, Runyi Yang, Bin Ren, Nicu Sebe, Ender Konukoglu, Luc van Gool, Danda Pani Paudel
Localizing textual descriptions within large-scale 3D scenes presents inherent ambiguities, such as identifying all traffic lights in a city.
no code implementations • 2 Dec 2024 • Ada-Astrid Balauca, Sanjana Garai, Stefan Balauca, Rasesh Udayakumar Shetty, Naitik Agrawal, Dhwanil Subhashbhai Shah, Yuqian Fu, Xi Wang, Kristina Toutanova, Danda Pani Paudel, Luc van Gool
In this work, we facilitate such reasoning by (a) collecting and curating a large-scale dataset of 65M images and 200M question-answer pairs in the standard museum catalog format for exhibits from all around the world; (b) training large vision-language models on the collected dataset; (c) benchmarking their ability on five visual question answering tasks.
no code implementations • 2 Dec 2024 • Anna-Maria Halacheva, Yang Miao, Jan-Nico Zaech, Xi Wang, Luc van Gool, Danda Pani Paudel
In this work, we address this shortcoming and introduce (1) an expertly curated dataset in the Universal Scene Description (USD) format, featuring high-quality manual annotations, for instance, segmentation and articulation on 280 indoor scenes; (2) a learning-based model together with a novel baseline capable of predicting part segmentation along with a full specification of motion attributes, including motion type, articulated and interactable parts, and motion parameters; (3) a benchmark serving to compare upcoming methods for the task at hand.
no code implementations • 2 Dec 2024 • Jiahuan Cheng, Jan-Nico Zaech, Luc van Gool, Danda Pani Paudel
A major reason for the success of 3DGS is its simplicity of representing a scene with a set of Gaussians, which makes it easy to interpret and adapt.
no code implementations • 28 Nov 2024 • Yuqian Fu, Runze Wang, Yanwei Fu, Danda Pani Paudel, Xuanjing Huang, Luc van Gool
In this paper, we focus on the Ego-Exo Object Correspondence task, an emerging challenge in the field of computer vision that aims to map objects across ego-centric and exo-centric views.
no code implementations • 27 Nov 2024 • Eduard Zamfir, Zongwei Wu, Nancy Mehta, Yuedong Tan, Danda Pani Paudel, Yulun Zhang, Radu Timofte
To address this, we introduce ``complexity experts" -- flexible expert blocks with varying computational complexity and receptive fields.
Ranked #2 on
Blind All-in-One Image Restoration
on 5-Degradations
1 code implementation • 25 Nov 2024 • Zuhao Liu, Aleksandar Yanev, Ahmad Mahmood, Ivan Nikolov, Saman Motamed, Wei-Shi Zheng, Xi Wang, Luc van Gool, Danda Pani Paudel
Advances in video generation have significantly improved the realism and quality of created scenes.
no code implementations • 20 Nov 2024 • Ashish Bastola, Nishant Luitel, Hao Wang, Danda Pani Paudel, Roshani Poudel, Abolfazl Razi
While deep learning models are powerful tools that revolutionized many areas, they are also vulnerable to noise as they rely heavily on learning patterns and features from the exact details of the clean data.
no code implementations • 4 Oct 2024 • Shi Chen, Danda Pani Paudel, Luc van Gool
The advancement of dense visual simultaneous localization and mapping (SLAM) has been greatly facilitated by the emergence of neural implicit representations.
no code implementations • 23 Sep 2024 • Sombit Dey, Jan-Nico Zaech, Nikolay Nikolov, Luc van Gool, Danda Pani Paudel
This is potentially caused by limited variations in the training data and/or catastrophic forgetting, leading to domain limitations in the vision foundation models.
no code implementations • 12 Sep 2024 • Asen Nachkov, Danda Pani Paudel, Luc van Gool
In this work, we leverage a differentiable simulator and design an analytic policy gradients (APG) approach to training AV controllers on the large-scale Waymo Open Motion Dataset.
1 code implementation • 3 Sep 2024 • Ada-Astrid Balauca, Danda Pani Paudel, Kristina Toutanova, Luc van Gool
In this work, we aim to adapt CLIP for fine-grained and structured -- in the form of tabular data -- visual understanding of museum exhibits.
no code implementations • 29 Aug 2024 • Nedyalko Prisadnikov, Wouter Van Gansbeke, Danda Pani Paudel, Luc van Gool
These contributions are: (i) a positional-embedding (PE) based loss for improved centroid regressions; (ii) Edge Distance Sampling (EDS) for the better separation of instance boundaries.
no code implementations • 20 Aug 2024 • Qi Ma, Yue Li, Bin Ren, Nicu Sebe, Ender Konukoglu, Theo Gevers, Luc van Gool, Danda Pani Paudel
In particular, we show that (1) the distribution of the optimized GS centroids significantly differs from the uniformly sampled point cloud (used for initialization) counterpart; (2) this change in distribution results in degradation in classification but improvement in segmentation tasks when using only the centroids; (3) to leverage additional Gaussian parameters, we propose Gaussian feature grouping in a normalized feature space, along with splats pooling layer, offering a tailored solution to effectively group and embed similar Gaussians, which leads to notable improvement in finetuning tasks.
1 code implementation • 17 Aug 2024 • Jiancheng Pan, Yanxing Liu, Yuqian Fu, Muyuan Ma, Jiaohao Li, Danda Pani Paudel, Luc van Gool, Xiaomeng Huang
Results demonstrate the advantages of the LAE-1M dataset and the effectiveness of the LAE-DINO method.
1 code implementation • 18 Jul 2024 • Bin Ren, Eduard Zamfir, Zongwei Wu, Yawei Li, Yidi Li, Danda Pani Paudel, Radu Timofte, Ming-Hsuan Yang, Nicu Sebe
With the proliferation of mobile devices, the need for an efficient model to restore any degraded image has become increasingly significant and impactful.
Ranked #5 on
5-Degradation Blind All-in-One Image Restoration
on 5-Degradation Blind All-in-One Image Restoration
5-Degradation Blind All-in-One Image Restoration
Benchmarking
+2
1 code implementation • 15 Jul 2024 • Pramish Paudel, Anubhav Khanal, Ajad Chhatkuli, Danda Pani Paudel, Jyoti Tandukar
In this paper, we present a fast, simple, yet effective method for creating animatable 3D digital humans from monocular videos.
1 code implementation • 8 Jul 2024 • Bin Ren, Guofeng Mei, Danda Pani Paudel, Weijie Wang, Yawei Li, Mengyuan Liu, Rita Cucchiara, Luc van Gool, Nicu Sebe
To answer this question, we first empirically validate that integrating MAE-based point cloud pre-training with the standard contrastive learning paradigm, even with meticulous design, can lead to a decrease in performance.
1 code implementation • 25 Jun 2024 • Qi Ma, Danda Pani Paudel, Ender Konukoglu, Luc van Gool
Neural implicit functions have demonstrated significant importance in various areas such as computer vision, graphics.
2 code implementations • 28 May 2024 • Yuedong Tan, Zongwei Wu, Yuqian Fu, Zhuyun Zhou, Guolei Sun, Eduard Zamfi, Chao Ma, Danda Pani Paudel, Luc van Gool, Radu Timofte
Technically, we achieve this by routing samples from one modality to the expert of the others, within a mixture-of-experts framework designed for multimodal video object tracking.
no code implementations • 24 May 2024 • Eduard Zamfir, Zongwei Wu, Nancy Mehta, Danda Pani Paudel, Yulun Zhang, Radu Timofte
Reconstructing missing details from degraded low-quality inputs poses a significant challenge.
Ranked #4 on
5-Degradation Blind All-in-One Image Restoration
on 5-Degradation Blind All-in-One Image Restoration
5-Degradation Blind All-in-One Image Restoration
Blind All-in-One Image Restoration
+1
no code implementations • 23 Dec 2023 • Rashik Shrestha, Bishad Koju, Abhigyan Bhusal, Danda Pani Paudel, François Rameau
This paper studies the problem of localizing cameras in NeRF using a diffusion model for camera pose adjustment.
no code implementations • 20 Dec 2023 • Junru Lin, Asen Nachkov, Songyou Peng, Luc van Gool, Danda Pani Paudel
In this work, we address the challenge of deploying Neural Radiance Field (NeRFs) in Simultaneous Localization and Mapping (SLAM) under the condition of lacking depth information, relying solely on RGB inputs.
no code implementations • 18 Dec 2023 • Asen Nachkov, Martin Danelljan, Danda Pani Paudel, Luc van Gool
For the enhanced safety of AVs, modeling perception uncertainty in BEV is crucial.
no code implementations • 13 Dec 2023 • M. Eren Akbiyik, Nedko Savov, Danda Pani Paudel, Nikola Popovic, Christian Vater, Otmar Hilliges, Luc van Gool, Xi Wang
In contrast, we focus on inferring the ego trajectory of a driver's vehicle using their gaze data.
1 code implementation • CVPR 2024 • Qi Ma, Danda Pani Paudel, Ajad Chhatkuli, Luc van Gool
In this paper, we showcase the effectiveness of optimizing monocular camera poses as a continuous function of time.
1 code implementation • CVPR 2024 • Zongwei Wu, Jilai Zheng, Xiangxuan Ren, Florin-Alexandru Vasluianu, Chao Ma, Danda Pani Paudel, Luc van Gool, Radu Timofte
In practice, most existing RGB trackers learn a single set of parameters to use them across datasets and applications.
Ranked #29 on
Rgb-T Tracking
on LasHeR
no code implementations • 23 Nov 2023 • Saman Motamed, Danda Pani Paudel, Luc van Gool
In this study, we introduce Lego, a textual inversion method designed to invert subject-entangled concepts from a few example images.
1 code implementation • 20 Nov 2023 • Nikola Popovic, Dimitrios Christodoulou, Danda Pani Paudel, Xi Wang, Luc van Gool
In this work, we propose to predict 3D eye gaze from weak supervision of eye semantic segmentation masks and direct supervision of a few 3D gaze vectors.
no code implementations • ICCV 2023 • Qi Ma, Danda Pani Paudel, Ajad Chhatkuli, Luc van Gool
In this work, we develop a novel method to model the deformable neural radiance fields using RGB and event cameras.
no code implementations • 25 Jul 2023 • Yigit Baran Can, Alexander Liniger, Danda Pani Paudel, Luc van Gool
Thus, online estimation of the lane graph is crucial for widespread and reliable autonomous navigation.
no code implementations • ICCV 2023 • Yigit Baran Can, Alexander Liniger, Danda Pani Paudel, Luc van Gool
In this work, we propose an architecture and loss formulation to improve the accuracy of local lane graph estimates by using 3D object detection outputs.
2 code implementations • 28 Apr 2023 • Zhuyun Zhou, Zongwei Wu, Danda Pani Paudel, Rémi Boutteau, Fan Yang, Luc van Gool, Radu Timofte, Dominique Ginhac
Subsequently, we devise EmoFormer, a novel network able to exploit the event data.
no code implementations • 3 Apr 2023 • Yigit Baran Can, Alexander Liniger, Danda Pani Paudel, Luc van Gool
One of the most common and useful representation of such an understanding is done in the form of BEV lane graphs.
1 code implementation • 22 Mar 2023 • Mohamad Shahbazi, Evangelos Ntavelis, Alessio Tonioni, Edo Collins, Danda Pani Paudel, Martin Danelljan, Luc van Gool
Pose-conditioned convolutional generative models struggle with high-quality 3D-consistent image generation from single-view datasets, due to their lack of sufficient 3D priors.
1 code implementation • ICCV 2023 • Zongwei Wu, Danda Pani Paudel, Deng-Ping Fan, Jingjing Wang, Shuo Wang, Cédric Demonceaux, Radu Timofte, Luc van Gool
In this work, we adapt such depth inference models for object segmentation using the objects' "pop-out" prior in 3D.
1 code implementation • ICCV 2023 • Nikola Popovic, Danda Pani Paudel, Luc van Gool
In this work, we aim to leverage the geometric prior of Manhattan scenes to improve the implicit neural radiance field representations.
no code implementations • 14 Nov 2022 • Yigit Baran Can, Alexander Liniger, Danda Pani Paudel, Luc van Gool
On the one hand, the proposed method learns to segment these planar hulls from the labeled data.
1 code implementation • 2 Aug 2022 • Zongwei Wu, Shriarulmozhivarman Gobichettipalayam, Brahim Tamadazte, Guillaume Allibert, Danda Pani Paudel, Cédric Demonceaux
In this work, we aim for RGB-D saliency detection that is robust to the low-quality depths which primarily appear in two forms: inaccuracy due to noise and the misalignment to RGB.
no code implementations • 3 Jun 2022 • Nikola Popovic, Danda Pani Paudel, Thomas Probst, Luc van Gool
It has since become a trend to use these five characteristics as a sufficient test, to determine whether or not gradient obfuscation is the main source of robustness.
1 code implementation • 11 May 2022 • Chuqiao Li, Zhiwu Huang, Danda Pani Paudel, Yabin Wang, Mohamad Shahbazi, Xiaopeng Hong, Luc van Gool
Within the proposed benchmark, we explore some commonly known essentials of standard continual learning.
no code implementations • 25 Mar 2022 • Ritika Chakraborty, Nikola Popovic, Danda Pani Paudel, Thomas Probst, Luc van Gool
However, multi-conditional image generation is a very challenging problem due to the heterogeneity and the sparsity of the (in practice) available conditioning labels.
1 code implementation • CVPR 2022 • Christoph Mayer, Martin Danelljan, Goutam Bhat, Matthieu Paul, Danda Pani Paudel, Fisher Yu, Luc van Gool
Optimization based tracking methods have been widely successful by integrating a target model prediction module, providing effective global reasoning by minimizing an objective function.
Ranked #5 on
Visual Object Tracking
on AVisT
2 code implementations • CVPR 2022 • Junjie Ye, Changhong Fu, Guangze Zheng, Danda Pani Paudel, Guang Chen
Previous advances in object tracking mostly reported on favorable illumination circumstances while neglecting performance at nighttime, which significantly impeded the development of related aerial robot applications.
1 code implementation • ICLR 2022 • Mohamad Shahbazi, Martin Danelljan, Danda Pani Paudel, Luc van Gool
On the contrary, we observe that class-conditioning causes mode collapse in limited data settings, where unconditional learning leads to satisfactory generative ability.
no code implementations • 30 Dec 2021 • Nikola Popovic, Danda Pani Paudel, Thomas Probst, Luc van Gool
We use linear layers with token-consistent stochastic parameters inside the multilayer perceptron blocks, without altering the architecture of the transformer.
no code implementations • 19 Dec 2021 • Yigit Baran Can, Alexander Liniger, Danda Pani Paudel, Luc van Gool
We use a Transformer-based architecture to detect the keypoints, as well as to summarize the visual context of the image.
1 code implementation • CVPR 2022 • Yigit Baran Can, Alexander Liniger, Danda Pani Paudel, Luc van Gool
We represent the road topology using a set of directed lane curves and their interactions, which are captured using their intersection points.
2 code implementations • ICCV 2021 • Yigit Baran Can, Alexander Liniger, Danda Pani Paudel, Luc van Gool
In this work, we study the problem of extracting a directed graph representing the local road network in BEV coordinates, from a single onboard camera image.
Ranked #3 on
Lane Detection
on nuScenes
1 code implementation • 10 Sep 2021 • Rui Gong, Martin Danelljan, Dengxin Dai, Danda Pani Paudel, Ajad Chhatkuli, Fisher Yu, Luc van Gool
In many real-world settings, the target domain task requires a different taxonomy than the one imposed by the source domain.
no code implementations • CVPR 2021 • Stefano d'Apolito, Danda Pani Paudel, Zhiwu Huang, Andres Romero, Luc van Gool
On the other hand, learning from inexpensive and intuitive basic categorical emotion labels leads to limited emotion variability.
no code implementations • 23 May 2021 • Guolei Sun, Yun Liu, Thomas Probst, Danda Pani Paudel, Nikola Popovic, Luc van Gool
This paper investigates the role of global context for crowd counting.
no code implementations • 18 May 2021 • Ankush Panwar, Pratyush Singh, Suman Saha, Danda Pani Paudel, Luc van Gool
The proposed method successfully adapts to the compound target domain consisting multiple new spoof types.
1 code implementation • CVPR 2021 • Suman Saha, Anton Obukhov, Danda Pani Paudel, Menelaos Kanakis, Yuhua Chen, Stamatios Georgoulis, Luc van Gool
Specifically, we show that: (1) our approach improves performance on all tasks when they are complementary and mutually dependent; (2) the CTRL helps to improve both semantic segmentation and depth estimation tasks performance in the challenging UDA setting; (3) the proposed ISL training scheme further improves the semantic segmentation performance.
1 code implementation • ICCV 2021 • Christoph Mayer, Martin Danelljan, Danda Pani Paudel, Luc van Gool
To tackle the problem of lacking ground-truth correspondences between distractor objects in visual tracking, we propose a training strategy that combines partial annotations with self-supervision.
Ranked #3 on
Video Object Tracking
on NT-VOT211
no code implementations • 26 Mar 2021 • Peshal Agarwal, Danda Pani Paudel, Jan-Nico Zaech, Luc van Gool
This paper aims at answering the question of finding the right strategy to make the target model robust and accurate in the setting of unsupervised domain adaptation without source data.
1 code implementation • CVPR 2021 • Mohamad Shahbazi, Zhiwu Huang, Danda Pani Paudel, Ajad Chhatkuli, Luc van Gool
To address this problem, we introduce a new GAN transfer method to explicitly propagate the knowledge from the old classes to the new classes.
no code implementations • ICCV 2021 • Guolei Sun, Thomas Probst, Danda Pani Paudel, Nikola Popovic, Menelaos Kanakis, Jagruti Patel, Dengxin Dai, Luc van Gool
Multiple tasks are performed by switching between them, performing one task at a time.
no code implementations • 31 Dec 2020 • Ayça Takmaz, Danda Pani Paudel, Thomas Probst, Ajad Chhatkuli, Martin R. Oswald, Luc van Gool
In this work, we present an unsupervised monocular framework for dense depth estimation of dynamic scenes, which jointly reconstructs rigid and non-rigid parts without explicitly modelling the camera motion.
no code implementations • 24 Dec 2020 • Dario Fuoli, Zhiwu Huang, Danda Pani Paudel, Luc van Gool, Radu Timofte
Video enhancement is a challenging problem, more than that of stills, mainly due to high computational cost, larger data volumes and the difficulty of achieving consistency in the spatio-temporal domain.
1 code implementation • CVPR 2021 • Nikola Popovic, Danda Pani Paudel, Thomas Probst, Guolei Sun, Luc van Gool
Learning to perform spatially distributed tasks is motivated by the frequent availability of only sparse labels across tasks, and the desire for a compact multi-tasking network.
no code implementations • CVPR 2021 • Rui Gong, Yuhua Chen, Danda Pani Paudel, Yawei Li, Ajad Chhatkuli, Wen Li, Dengxin Dai, Luc van Gool
Open compound domain adaptation (OCDA) is a domain adaptation setting, where target domain is modeled as a compound of multiple unknown homogeneous domains, which brings the advantage of improved generalization to unseen domains.
1 code implementation • NeurIPS 2020 • Janine Thoma, Danda Pani Paudel, Luc V. Gool
Our soft assignment makes a gradual distinction between close and far images in both geometric and feature spaces.
1 code implementation • 19 Oct 2020 • Siwei Zhang, Zhiwu Huang, Danda Pani Paudel, Luc van Gool
In our formulation, we exploit a new method to enable the emotion prediction and the joint distribution learning in a unified adversarial learning game.
1 code implementation • 27 Aug 2020 • Janine Thoma, Danda Pani Paudel, Ajad Chhatkuli, Luc van Gool
Image features for retrieval-based localization must be invariant to dynamic objects (e. g. cars) as well as seasonal and daytime changes.
no code implementations • 4 Jul 2020 • Rui Gong, Danda Pani Paudel, Ajad Chhatkuli, Luc van Gool
In this paper, we propose a unified SfM method, in which the matching process is supported by self-calibration constraints.
1 code implementation • 21 Mar 2020 • Janine Thoma, Danda Pani Paudel, Ajad Chhatkuli, Luc van Gool
This is achieved by guiding the learning process such that the feature and geometric distances between images are directly proportional.
1 code implementation • ECCV 2020 • Clara Fernandez-Labrador, Ajad Chhatkuli, Danda Pani Paudel, Jose J. Guerrero, Cédric Demonceaux, Luc van Gool
This paper aims at learning category-specific 3D keypoints, in an unsupervised manner, using a collection of misaligned 3D point clouds of objects from an unknown category.
no code implementations • 15 Dec 2019 • Suman Saha, Wen-Hao Xu, Menelaos Kanakis, Stamatios Georgoulis, Yu-Hua Chen, Danda Pani Paudel, Luc van Gool
Face anti-spoofing is a measure towards this direction for bio-metric user authentication, and in particular face recognition, that tries to prevent spoof attacks.
no code implementations • 23 Oct 2019 • Zhiwu Huang, Danda Pani Paudel, Guanju Li, Jiqing Wu, Radu Timofte, Luc van Gool
This paper introduces a divide-and-conquer inspired adversarial learning (DACAL) approach for photo enhancement.
no code implementations • ICCV 2019 • Thomas Probst, Danda Pani Paudel, Ajad Chhatkuli, Luc van Gool
Notably, we further exploit the POP formulation of non-minimal solver also for the generic consensus maximization problems in 3D vision.
1 code implementation • CVPR 2019 • Jiqing Wu, Zhiwu Huang, Dinesh Acharya, Wen Li, Janine Thoma, Danda Pani Paudel, Luc van Gool
In generative modeling, the Wasserstein distance (WD) has emerged as a useful metric to measure the discrepancy between generated and real data distributions.
Ranked #1 on
Video Generation
on TrailerFaces
no code implementations • CVPR 2019 • Janine Thoma, Danda Pani Paudel, Ajad Chhatkuli, Thomas Probst, Luc van Gool
The problem of localization often arises as part of a navigation process.
1 code implementation • 4 Oct 2018 • Dinesh Acharya, Zhiwu Huang, Danda Pani Paudel, Luc van Gool
Furthermore, we introduce a sliced version of Wasserstein GAN (SWGAN) loss to improve the distribution learning on the video data of high-dimension and mixed-spatiotemporal distribution.
no code implementations • ECCV 2018 • Danda Pani Paudel, Luc van Gool
This paper addresses the problem of robustly autocalibrating a moving camera with constant intrinsics.
no code implementations • ECCV 2018 • Thomas Probst, Danda Pani Paudel, Ajad Chhatkuli, Luc van Gool
In this paper we present a method for incremental Non-Rigid Structure-from-Motion (NRSfM) with the perspective camera model and the isometric surface prior with unknown focal length.
no code implementations • ECCV 2018 • Thomas Probst, Ajad Chhatkuli, Danda Pani Paudel, Luc van Gool
In this paper, we formulate the model-free consensus maximization as an Integer Program in a graph using `rules' on measurements.
no code implementations • 4 Dec 2017 • Zhiwu Huang, Bernhard Kratzwald, Danda Pani Paudel, Jiqing Wu, Luc van Gool
This paper presents a new problem of unpaired face translation between images and videos, which can be applied to facial video prediction and enhancement.
1 code implementation • 30 Nov 2017 • Bernhard Kratzwald, Zhiwu Huang, Danda Pani Paudel, Acharya Dinesh, Luc van Gool
In this paper, we aim to improve the state-of-the-art video generative adversarial networks (GANs) with a view towards multi-functional applications.
no code implementations • ICCV 2017 • Danda Pani Paudel, Adlane Habed, Luc van Gool
This paper addresses the problem of estimating the geometric transformation relating two distinct visual modalities (e. g. an image and a map, or a projective structure and a Euclidean 3D model) while relying only on semantic cues, such as semantically segmented regions or object bounding boxes.
no code implementations • CVPR 2017 • Pablo Speciale, Danda Pani Paudel, Martin R. Oswald, Till Kroeger, Luc van Gool, Marc Pollefeys
While randomized methods like RANSAC are fast, they do not guarantee global optimality and fail to manage large amounts of outliers.
1 code implementation • 8 Jun 2017 • Jiqing Wu, Zhiwu Huang, Dinesh Acharya, Wen Li, Janine Thoma, Danda Pani Paudel, Luc van Gool
In generative modeling, the Wasserstein distance (WD) has emerged as a useful metric to measure the discrepancy between generated and real data distributions.
no code implementations • ICCV 2015 • Danda Pani Paudel, Adlane Habed, Cedric Demonceaux, Pascal Vasseur
This paper deals with the problem of registering a known structured 3D scene and its metric Structure-from-Motion (SfM) counterpart.
no code implementations • CVPR 2015 • Danda Pani Paudel, Adlane Habed, Cedric Demonceaux, Pascal Vasseur
This paper investigates the problem of registering a scanned scene, represented by 3D Euclidean point coordinates, and two or more uncalibrated cameras.
no code implementations • CVPR 2014 • Adlane Habed, Danda Pani Paudel, Cedric Demonceaux, David Fofi
We present a new globally optimal algorithm for self-calibrating a moving camera with constant parameters.